Hyperspectral imaging (HSI) has been extensively utilized for a number of
real-world applications. HSI classification (HSIC) is a challenging task due to
high inter-class similarity, high intra-class variability, overlapping, and
nested regions. A 2D Convolutional Neural Network (CNN) is a viable approach
whereby HSIC highly depends on both Spectral-Spatial information, therefore, 3D
CNN can be an alternative but highly computational complex due to the volume
and spectral dimensions. Furthermore, these models do not extract quality
feature maps and may underperform over the regions having similar textures.
Therefore, this work proposed a 3D CNN model that utilizes both
spatial-spectral feature maps to attain good performance. In order to achieve
the said performance, the HSI cube is first divided into small overlapping 3D
patches. Later these patches are processed to generate 3D feature maps using a
3D kernel function over multiple contiguous bands that persevere the spectral
information as well. Benchmark HSI datasets (Pavia University, Salinas and
Indian Pines) are considered to validate the performance of our proposed
method. The results are further compared with several state-of-the-art methods.

本论文提出了一种利用 3D CNN 模型用于 HSI 分类的方法，其利用了空间光谱特征图以实现良好的性能，并对其性能进行了实验验证。

一种用于高光谱图像分类的快速三维卷积神经网络

A Fast 3D CNN for Hyperspectral Image Classification

In contrast to the literature where local patterns in 3D point clouds are
captured by customized convolutional operators, in this paper we study the
problem of how to effectively and efficiently project such point clouds into a
2D image space so that traditional 2D convolutional neural networks (CNNs) such
as U-Net can be applied for segmentation. To this end, we are motivated by
graph drawing and reformulate it as an integer programming problem to learn the
topology-preserving graph-to-grid mapping for each individual point cloud. To
accelerate the computation in practice, we further propose a novel hierarchical
approximate algorithm. With the help of the Delaunay triangulation for graph
construction from point clouds and a multi-scale U-Net for segmentation, we
manage to demonstrate the state-of-the-art performance on ShapeNet and PartNet,
respectively, with significant improvement over the literature. Code is
available at this https URL

本文研究了点云数据如何被有效和高效地投影到 2D 图像空间中，以便于利用传统的 2D 卷积神经网络进行分割，通过引入具有拓扑结构保持特性的图形处理方法，并使用分层逼近算法，结合 Delaunay 三角剖分技术和多尺度 U-Net 网络进行图像分割，取得了 ShapeNet 和 PartNet 的最新成果。